Abstract

Nicotinamide N-methyltransferase (NNMT) is overexpressed in a variety of human cancers, where it contributes to tumorigenesis by a still poorly understood mechanism. Here, we show using metabolomics that NNMT impairs the methylation potential of cancer cells by consuming methyl units from S-adenosyl methionine to create the stable metabolic product 1-methylnicotinamide. As a result, NNMT-expressing cancer cells possess an altered epigenetic state that includes hypomethylated histones and other cancer-related proteins combined with heightened expression of pro-tumorigenic gene products. Our findings thus point to a direct mechanistic link between the deregulation of a metabolic enzyme and widespread changes in the methylation landscape of cancer cells.

Building on the classical studies by Warburg showing that cancer cells exhibit enhanced dependence on glycolysis1, researchers over the past decade have uncovered a diverse array of metabolic changes that support tumorigenesis2, 3, and altered metabolism is now considered a hallmark of cancer4. Metabolism is tightly linked with signaling and transcriptional networks, as well as cellular epigenetic machinery and the post-translational modification state of proteins5. Recent data support the concept that metabolic reprogramming in tumor cells is driven by a number of biochemical changes, including activation of oncogenes, inactivation of tumor suppressors, and pro-tumorigenic mutations in metabolic enzymes themselves5, 6. Reciprocally, signaling and transcriptional pathways can be regulated by metabolism5. Large-scale profiling experiments, including genomics7, 8 and proteomics9, have uncovered many metabolic enzymes that show altered expression in cancers. Nonetheless, the biochemical and cellular functions that these deregulated enzymes play in cancer often remain unknown. Here, we investigate the function of one such cancer-associated metabolic enzyme nicotinamide N-methyltransferase (NNMT) and show through metabolomics that it exerts specific control over the methylation potential of cancer cells and, through doing so, imparts a broad effect on their epigenetic state.

RESULTS

NNMT activity correlates with cancer aggressiveness

NNMT is a cytosolic enzyme that catalyzes the transfer of methyl group from S-adenosyl-L-methionine (SAM) to nicotinamide (NA), generating S-adenosylhomocysteine (SAH) and 1-methylnicotinamide (1MNA). NNMT is overexpressed in a variety of tumors, including cancers of the lung, liver, kidney, bladder, and colon, and has been shown to promote the migration, invasion, proliferation, and survival of cancer cells10–14. Despite considerable experimental evidence that NNMT supports tumorigenesis and may thus serve as a potential anticancer target, the actual metabolic functions played by this enzyme in cancer cells have not yet been determined. We set out to address this question by first profiling NNMT expression and activity across a panel of aggressive and non-aggressive human cancer cell lines from multiple tumors of origin, including ovarian (aggressive SKOV3, non-aggressive OVCAR3), kidney (aggressive 786O, non-aggressive 769P), lung (aggressive H226, non-aggressive H522) and uveal melanoma (aggressive C8161, non-aggressive MUM2C). Differences in the aggressive properties of these cancer cell lines have been previously established by measuring their respective in vitro migration/invasion and in vivo tumor-growth activities15 (also see Supplementary Results, Supplementary Fig. 1). Consistent with past studies11, 14, we found that NNMT expression and activity were highly elevated in aggressive cancer cells compared to their non-aggressive counterparts (Fig. 1a). We also found that aggressive cancer lines possessed higher levels of the NNMT-catalyzed product 1MNA (Fig. 1b). We next stably overexpressed NNMT in non-aggressive cancer lines (NNMT-OE cells) and generated two control cell populations that overexpressed GFP (GFP-OE cells) and an inactive NNMT mutant (Y20A16; Y20A-OE cells), respectively (Fig. 1c and Supplementary Fig. 2). NNMT-OE cells, but not GFP-OE or Y20A-OE cells showed dramatic increases in NNMT activity and cellular levels of 1MNA (Fig. 1c and Supplementary Fig. 2). NNMT-OE cells also displayed enhanced migration compared to control cells (Supplementary Fig. 3).

We also produced a complementary cell model where NNMT was knocked-down by > 75% in the aggressive ovarian cancer cell line SKOV3 using siRNA methods and found that these si-NNMT cells exhibited decreased NNMT activity and reduced 1MNA levels compared to SKOV3 cells treated with a scrambled siRNA control probe (si-Control cells) (Fig. 1d).

NNMT impairs the methylation potential of cancer cells

Next, we tested whether the NNMT product 1MNA might itself be responsible for the enhanced migratory activity of NNMT-OE cells. However, treatment of non-aggressive cancer cells with 1MNA did not affect their migration (Supplementary Fig. 4). This result motivated us to consider more broadly the impact of NNMT on the metabolic state of cancer cells, which we examined by performing a global analysis of polar metabolites17 in NNMT-OE and control cells. We were particularly interested in identifying metabolic changes caused by NNMT in multiple cancer cell types, as we considered such consistent alterations as having the highest probability of reflecting a conserved functional role for NNMT in cancer metabolism. To address this problem, we employed an untargeted metabolomics approach18, where metabolomes from NNMT-OE and GFP-OE renal carcinoma (769P), ovarian cancer (OVCAR3), and melanoma (MUM2C) cells were comparatively analyzed by an HPLC-Q-TOF-MS system operating in the broad mass scanning mode (m/z range of 50–1200 Da). Metabolites with significantly differing levels in NNMT-OE and GFP-OE cells were identified using the XCMS analyte profiling software19, which aligns and quantifies the relative signal intensities of mass peaks from multiple LC-MS traces (Supplementary Dataset 1). We then clustered the changing metabolites into groups based on whether they appeared in one, two, or all three pairs of NNMT-OE /GFP-OE comparisons. The resulting Venn diagram revealed only two metabolites that were consistently deregulated across all three types of cancer (Fig. 2a). These metabolites, which were both significantly elevated in NNMT-OE cells, were identified by a combination of high-resolution MS (Fig. 2b and Supplementary Fig. 5), tandem MS analysis (Fig. 2c,d), and LC-migration (Fig. 2e,f) as 1MNA (m/z measured = 137.068943; m/z predicted = 137.070939) and SAH (m/z measured = 385.122400; m/z predicted = 385.128845).

Structural assignment of 1MNA and SAH as deregulated metabolites in NNMT-OE cells

Targeted LC-MS profiling with a deuterated internal standard confirmed elevated SAH levels in NNMT-OE cells compared to both GFP-OE and Y20A-OE control cells (Fig. 3a and Supplementary Fig. 5). On the other hand, SAM levels were relatively unchanged (fold < 1.5) in NNMT-OE versus control cells (Fig. 3a). We wondered whether the lack of effect of NNMT expression on SAM might be due to the high endogenous concentrations of this metabolite in cancer cells grown in standard culture medium, which contains 100 μM methionine, a concentration that is 5–10 times higher than blood levels of this amino acid in humans20. Lowering the methionine concentration in media to 10–20 μM caused a corresponding dramatic reduction in cellular methionine and SAM levels, while SAH levels were less affected (Fig. 3b). NNMT-OE 769P cells grown in 20 μM methionine continued to show significantly elevated SAH levels, as well as a modest, but significant reduction in SAM levels, compared to control cells (Supplementary Fig. 6). Growth in 10 μM methionine media produced a more dramatic shift in metabolic profile such that NNMT-OE 769P cells now showed both a significant reduction in SAM levels and significant increase in SAH levels compared to control cells (Fig. 3c). Similar changes in SAM and SAH were observed in NNMT-OE 769P cells when processed by one of multiple metabolite extraction protocols (harvesting cells in cold PBS followed by metabolite extraction versus direct extraction of metabolites from plated cells without any PBS washes; Supplementary Fig. 7). NNMT-OE MUM2C cells maintained high SAH and unchanged SAM levels at all tested methionine concentrations (Supplementary Fig. 5 and 8). Importantly, in all human cancer cell types and at all methionine concentrations tested, elevated levels of NNMT resulted in more than a twofold reduction in cellular methylation potential (MP), defined as the ratio of SAM:SAH (Supplementary Table 1; see below for more discussion of MP). Conversely, si-NNMT SKOV3 cells showed elevated SAM and reduced SAH levels to produce a net four-fold increase in MP compared to si-Control cells (Fig. 3d).

1MNA is a stable metabolic product in cancer cells

Taken together, our metabolomic findings revealed that the NNMT-catalyzed conversion of nicotinamide to 1MNA exerted a more global impact on the MP of cancer cells. This result, combined with the lack of effect of 1MNA on the migratory activity of cancer cells, led us to consider that this metabolite, rather than functioning as a pro-tumorgenic signal, might instead act as a sink for storing methylation units in cancer cells that express high levels of NNMT. To further investigate such a model, we performed metabolic labeling studies using deuterated 1MNA and found that this metabolite is remarkably stable in cancer cells, as we did not detect its conversion to other deuterated metabolites even after incubation with cancer cells for 24 h (Fig. 3e and Supplementary Fig. 9). In contrast, deuterated nicotinamide (NA) was transformed by cancer cells into numerous metabolic products, including NAD+, NADH, nicotinamide mononucleotide (NMN), and 1MNA (Fig. 3e and Supplementary Fig. 9). These data indicate that cancer cells have a limited capacity to recover the methylation units that accumulate in 1MNA as a result of NNMT catalysis, offering a satisfying biochemical mechanism to explain the effect of this enzyme on cellular MP.

NNMT regulates protein methylation in cancer cells

The MP of cells has the potential to influence a variety of important pathways and processes, including the modification states of proteins, DNA, RNA, and lipid metabolites, which are regulated by methyltransferases that utilize SAM as a universal methyl donor21–24. SAH is a product of these transmethylation reactions and acts as a competitive inhibitor of methyltransferases25. For this reason, MP is an important metabolic indicator of cellular methylation status, and a decrease in this parameter has been associated with a global hypomethylation state in cells22. We therefore next tested whether NNMT-induced changes in MP affect protein methylation, focusing on methylation events that have been linked to cancer, such as those that occur on histones26, 27. When grown in media containing 10 and 20 μM methionine, NNMT-OE cells showed a significant decrease in many (e.g., H3K4, H3K9, H3K27 and H4K20), but not all (e.g., H3R17) of the tested histone methylation events compared to control (GFP-OE or Y20A-OE) cells (Fig. 4a,b and Supplementary Fig. 6). This effect was most dramatic in 769P cells (Fig. 4a,b and Supplementary Fig. 6), but also observed in MUM2C cells (Supplementary Fig. 8), correlating with the greater change in MP caused by NNMT expression in 769P cells (four-fold change, compared to a two-fold change in MUM2C cells). Conversely, si-NNMT cells showed increased levels of histone methylation events compared to si-Control cells (Fig. 4a,b and Supplementary Fig. 8). We also noted that the siControl-SKOV3 cells exhibited lower basal levels of histone methylation compared to the control (GFP-OE, Y20-OE) 769P cells (Fig. 4b), consistent with the higher endogenous levels of NNMT (Fig. 1c,d) controlling histone methylation in SKOV3.

NNMT regulates the methylation state of histones and other signaling proteins in cancer cells

The histone methylation changes observed in NNMT-OE cells were mostly blunted when these cells were grown on high methionine (100 μM, Supplementary Fig. 10) despite their altered MP (Supplementary Table 1). This result suggests that the methylation state of cells may be tied to more than just their relative MP values, but also to the absolute SAM and SAH concentrations that form the basis for this ratiometric measurement. We further found that 1MNA (0.5 mM) did not alter histone methylation when added to GFP-OE 769P cells grown on low (10 μM) methionine (Supplementary Fig. 11), providing additional support that NNMT affects protein methylation by altering SAM and SAH rather than through a direct action of its product 1MNA.

We next tested whether the impact of NNMT on cellular methylation extended beyond histones to include other proteins and biomolecules. The tumor suppressor protein phosphatase 2A (PP2A), is regulated by methylation of the C-terminal Leu309 residue of its catalytic subunit28, and a decrease in PP2A methylation has been shown to promote basal ERK pathway activity required for efficient growth factor responses in cancer cells29. We found that NNMT-OE cells show a striking reduction in methylated PP2A and a corresponding increase in demethylated PP2A compared to control cells (Fig. 4b, c). si-NNMT cells conversely displayed higher methylated PP2A compared to si-Control cells (Fig. 4c). We then more globally assessed the effect of NNMT on arginine methylation of proteins using the ASYM25 antibody, which is specific for asymmetrical dimethylated arginines30, and found that several, but not all cellular proteins showed lower arginine methylation levels in NNMT-OE cells compared to control cells (Supplementary Fig. 12). si-NNMT cells showed more limited changes in arginine methylation, but the observed effects trended, as expected, toward increases in methylation levels (Supplementary Fig. 12). In contrast, NNMT overexpression or knockdown did not affect global DNA methylation as measured by total cellular 5-methyl-2′-deoxycytidine content (Supplementary Fig. 12).

We evaluated whether NNMT-induced changes in histone methylation correlated with changes in gene expression by performing DNA microarray experiments comparing NNMT-OE and Y20A-OE 769P cells (Supplementary Dataset 2). These global profiling studies identified several cancer-related genes with altered expression in NNMT-OE cells, including SNAI231, TGFB232, CNTN133, ADAMTS634 and LAMB335. We used quantitative RT-PCR to confirm higher expression levels for all five genes in NNMT-OE compared to Y20A-OE or parental 769P cancer cells (Fig. 4d). Notably, methylation of H3K27 has previously been shown to regulate the SNAI2 and CNTN1 genes in human endothelial cells36 and the LAMB3 gene in breast cancer cells37. Upregulation of SNAI2 and TGFB2 gene expression has also been observed in breast cancer cells treated with the global methyltransferase inhibitor 3-deazaneplanocin A, but not in cells treated with the DNA methylation inhibitor 5-azacytidine38. These past findings provide supportive evidence that the gene expression changes observed in our study are likely due to reductions in MP and histone methylation in NNMT-OE cells. Finally, we found that NNMT-regulated changes in protein methylation observed on low methionine medium correlated with altered cancer cell behavior, as NNMT-OE and si-NNMT cells showed enhanced migration and reduced migration/invasion, respectively, compared to their corresponding control cells (Supplementary Fig. 3). In contrast, si-NNMT cells grown on high methionine medium showed no change in migration capacity compared to si-Control cells (Supplementary Fig. 3).

DISCUSSION

Deregulated metabolic pathways can affect cancer cell biology in a number of ways that extend beyond simply providing energy and primary building blocks to support proliferation. Recent studies on mutant forms of isocitrate dehydrogenase-1 (IDH1) associated with glioma have, for instance, provided evidence that these IDH1 variants affect the methylation state of histones and DNA39 possibly by generating the novel metabolite 2-hydroxyglutarate40, which can competitively inhibit α-ketoglutarate-dependent demethylase enzymes41. Here, we show that NNMT, which is overexpressed in a diverse set of cancers, regulates the protein methylation state of tumor cells through a distinct mechanism that involves altering cellular ratios of SAM:SAH. In this model, 1MNA, the primary product of NNMT catalysis, serves principally as a stable sink to store methylation units in tumor cells rather than as a “bioactive” pro-tumorigenic metabolite, allowing NNMT to act in turn as a metabolic rheostat that can fine-tune the methylation state of cancer cells. A similar model has been proposed to explain the function of glycine-N-methyltransferase in normal liver, where this enzyme regulates MP42, although its downstream impact on liver protein methylation has not, to our knowledge, been examined. Interestingly, administration of nicotinamide to GNMT−/− mice can rectify their aberrantly high liver levels of SAM42, suggesting that, in the absence of GNMT, NNMT may regulate the MP of normal tissues. Metabolic pathways that are upstream of SAM biosynthesis may also influence MP and protein methylation, as has recently been demonstrated for threonine metabolism in mouse embryonic stem cells43. Finally, we should also emphasize that we observed an effect of NNMT on protein methylation when cancer cells were cultured in low (10–20 μM), but not high (100 μM) concentrations of methionine. These findings serve as a reminder that culturing conditions can exert a substantial influence over the metabolic and protein modification state of cancer cells. In this regard, it would be important, in future studies, to examine the impact of nicotinamide concentrations on NNMT-regulated methylation pathways in cancer cells. Nicotinamide levels have been reported to range from 0.4 to 400 μM in mammalian tissues and fluids44–46, and nicotinamide concentration was 8 μM in the RPMI media used in this study. Under conditions where nicotinamide is more limiting, it is possible that NNMT could alter additional metabolic parameters, such as NAD+/NADH ratios. Nicotinamide is also under clinical investigation as a sensitizer for radiotherapy in a variety of cancers47. Our data suggest that exogenously administered nicotinamide could have effects beyond radiosensitization and directly impact the metabolism and biology of tumors, especially if they express high levels of NNMT.

Projecting forward, it would be helpful to better understand how NNMT is regulated in cancer cells. We are not aware of reported somatic mutations or evidence of gene amplification for NNMT in cancer, but recent studies indicate that Ras48, Stat349, and NF-κB50 signaling pathways may be responsible for driving NNMT overexpression in cancer cells, where its heightened levels appear to correlate with epithelial-to-mesenchymal transition8, 48. We further found that NNMT does not regulate all histone methylation events or global DNA methylation, which suggests that the enzyme selectively impacts some, but not all cellular methylation pathways, possibly depending on the relative Km and Ki values of individual methyltransferase enzymes (including NNMT itself) for SAM and SAH, respectively. Consistent with this premise, we found that methylation events that are controlled by methyltransferases with higher Km values (and Ki or IC50 values) for SAM (and SAH) tended to be more sensitive to NNMT (Supplementary Table 2). Finally, we should emphasize that further studies are required to understand the functional relationship between the specific protein methylation and gene expression changes and pro-tumorigenic effects caused by NNMT in cancer cells. Our findings so far suggest that the methylation events regulated by NNMT can alter histone-dependent gene expression, but also extend beyond histones to include tumor suppressor proteins like PP2A. Given its apparently widespread influence over protein methylation patterns in tumor cells, NNMT could represent an attractive drug target to modulate the cancer epigenome for therapeutic benefit.

General synthetic methods

1H NMR and 13C NMR spectra were recorded on Bruker DRX-600 spectrometers using residual solvent peak as an internal standard. NMR chemical shifts are reported in ppm using residual solvent peak as internal standard, and J values are reported in Hz. High resolution mass spectra were recorded on an Agilent 6520 mass spectrometer using ESI-TOF.

The cDNA clone of human NNMT (BC000234, Open BioSystems) in pOTB7 was subcloned into pET-45b (+) vector for further manipulations. Catalytically inactive NNMT-Y20A mutant was generated by QuikChange site-directed mutagenesis using the primer 5′-atctaagccattttaaccctcgggatgccctagaaaaatattacaagtttg-3′ and its complement. Wildtype and mutant NNMT were cloned into modified pCLNCX retroviral vector 53. Retrovirus was prepared by taking 1.5 μg of both pCLNCX and pCL-Ampho vectors and 20 μl of FuGENE HD reagent (Roche) to transfect 80% confluent HEK293T cells. Virus containing supernatant from day 2 was collected and, in the presence of 8 μg/ml polybrene, used to stably infect cells for 72 h. Infection was followed by 7–14 days of selection in medium containing hygromycin B (100 μg/ml). Cells were expanded and cultured in complete RPMI-1640.

For all experiments except for studies in low methionine medium, cells were seeded at 4 × 106 cells/150 mm dish and were allowed to proliferate in complete RPMI for 16–24 h. For studies performed in low (10 μM and 20 μM) methionine medium, cells were washed with PBS, seeded at a concentration of 2 × 106 cells/150 mm dish, and cultured for 48h in methionine-free RPMI supplemented with indicated amounts of methionine.

NNMT activity assay

Cell pellets were resuspended in either 50 mM Tris-HCl, pH 8.0 or PBS (lung carcinoma lines), followed by sonication and centrifugation at 16,000 g for 10 min. Lysates (70–120 μg) were incubated with a reaction mixture (200 μM d4-NA, 50 μM S-adenosyl-L-methionine (SAM) and 2 mM DTT) at room temperature for 15–30 min in a volume of 20 μl. Reactions were quenched with equal amount of methanol, followed by a 10 min centrifugation at 16,000 g. Formation of d4-1MNA was followed by targeted LC-MS analysis. Briefly, 1MNA-d4 was separated with a Luna-NH2 column (5 μm, 100A, 50 × 4.6 mm, Phenomenex) together with a pre-column (NH2, 4 × 3.0 mm). Mobile phase A was composed of 100% CH3CN containing 0.1% formic acid, and mobile phase B was composed of 95:5 v/v H2O:CH3CN supplemented with 50 mM NH4OAc and 0.2% NH4OH. The flow rate started at 0.1 ml/min and the gradient consisted of 5 min 0% B, a linear increase to 100% B over 15 min at a flow rate of 0.4 ml/min, followed by an isocratic gradient of 100% B for 15 min at 0.5 ml/ml before equilibrating for 5 min at 0% B at 0.4 ml/min (40 min total). For each run the ejection volume was 20 μl. MS analysis was performed on an Agilent G6410B tandem mass spectrometer with ESI source. The dwell time for d4-1MNA was set to 100 ms. The capillary was set to 4 kV, the fragmentor was set to 100 V. The drying gas temperature was 350 °C, the drying gas flow rate was 11 l/min, and the nebulizer pressure was 35 psi. The mass spectrometer was run in MRM mode, monitoring the transition of m/z from 141 to 98 for d4-1MNA (positive ionization mode).

RNA interference studies in human cancer cell lines

Hs-NNMT-8 (si-NNMT, cagctactacatgattggtga) and Ctrl-AllStars-1 (si-Control, siRNA that has no homology to any known mammalian gene) were purchased from QIAGEN as FlexiTube siRNA premix. Cells were seeded at 0.25 × 106 cells/100 mm dish followed by treatment with siRNA premix reagent. Cells were cultured in complete RPMI for 72 h and tested for the loss of NNMT activity.

For studies performed in high and low methionine medium, 72 h after transfection complete RPMI medium was exchanged with RPMI containing indicated amount of methionine and cells were allowed to proliferate for additional 24 h. NNMT knockdown was confirmed in each experiment.

Untargeted metabolomic analysis of cancer cell lines

GFP-OE and NNMT-OE human cell lines were seeded at 4 × 106 cells/150 mm dish and cultured in complete RPMI medium for 24 h followed by 4 h serum starvation. Cells were scraped into ice-cold PBS and isolated by centrifugation at 1,400 × g at 4 °C. Water soluble cellular metabolites were extracted using methanol-water extraction protocol, essentially as previously described17. In brief, cell pellets were re-suspended in 100 μl of a 80:20 mixture of MeOH:H2O. For some experiments, internal deuterated standards, including 1 nmol d4-NA, 0.1 nmol d4-1MNA, 0.1 nmol 13Cd3-methionine, 0.1 nmol d3-SAM, 0.1 nmol d4-SAH, and 10 nmol d3-serine, were added to the extraction solution, for absolute quantification and sample normalization. The mixture was sonicated for 5 s followed by a 10 min centrifugation at 16,000 × g. The supernatant was collected and stored at −80° C or injected directly into mass spectrometer (30 μl). Metabolites were also extracted by an alternative protocol involving direct scrapping into organic solvent54 for the data shown in Supplementary Fig. 7. GFP-OE and NNMT-OE samples (3–4 replicates/line) were run sequentially. Water soluble cellular metabolites were separated by hydrophilic interaction chromatography17 with a Luna-NH2 column together with a pre-column. Mobile phase A was composed of 100% CH3CN, and mobile phase B was composed of 95:5 v/v H2O:CH3CN. Both solvents were supplemented with 0.1% formic acid to assist ion formation in a positive mode. For negative mode analysis, mobile phase B was supplemented with 50 mM NH4OAc and 0.2% NH4OH. The flow rate started at 0.1 ml/min for 5 min. The gradient started with 0% B for 5 min and increased linearly to 100% B over 40 min with a flow rate of 0.4 ml/min, followed by an isocratic gradient of 100% B for 10 min at 0.5 ml/min. Then column was equilibrated with 0% B for 5 min at 0.4 ml/min. MS analysis, scanning from m/z = 50–1200, was performed on Agilent 6520 Accurate Mass Q-TOF with ESI source. Untargeted LC-MS analysis was performed in both positive and negative ionization mode. The capillary was set to 4 kV. The drying gas temperature was 350 °C, the drying gas flow rate was 11 l/min, and the nebulizer pressure was 45 psi.

To identify metabolites with differential levels in NNMT-OE versus GFP-OE cells, we employed XCMS analyte profiling software. In brief, XCMS identifies features whose relative intensity varies between sample groups (group 1: NNMT-OE replicates; group2: GFP-OE replicates) and calculates fold changes, as well as P-values. XCMS software allows quick access to the quality of each feature by generating extracted ion chromatograms display panels (see Fig. 2b and Supplementary Fig. 5). Obtained data sets were first filtered based on P-value (P < 0.01) and fold change (fold > 2). Significant peak changes between samples were confirmed by manually extracting MS1 signals and by calculating the area under the peak from MS1 chromatograms. We next clustered the changing metabolites into groups based on whether they appear in one, two, or all three (769P, MUM2C and OVCAR3) pairs of NNMT-OE/GFP-OE. Prioritization was given to those metabolites that were found to change in all three cancer cell line sets. Two metabolites identified as 1MNA and SAH (see Fig. 2) were consistently deregulated across all three cell lines (Supplementary Dataset 1).

In addition to ions corresponding to endogenous 1MNA and SAH, an ion with m/z value of 152.074417 was also consistently elevated in all NNMT-OE lines compared to GFP-OE lines. This ion was identified as 3-methoxycarbonyl-1-methylpyridinium by using a combination of high-resolution MS (observed m/z = 152.074417, calculated m/z = 152.070605) and co-elution with authentic sample of 3-methoxycarbonyl-1-methylpyridinium. Elevated levels of 3-methoxycarbonyl-1-methylpyridinium in NNMT-OE metabolomes compared to GFP-OE metabolomes are likely due to the slow alcoholysis of NNMT product 1MNA in the methanolic extracts, as no 152.074417 ion was detected in metabolomes from GFP-OE and NNMT-OE cells when cell pellets were extracted with a 50:50 mixture of CH3CN:H2O. In addition, formation of this ester from synthetic 1MNA was also observed in methanolic solutions containing residual amounts of PBS without cellular extracts, confirming the non-metabolic origin of the compound. Interestingly, this ester is not formed in pure 80:20 MeOH:H2O mixture, suggesting that certain components of residual PBS catalyze the solvolysis. Considering the non-metabolic nature of this compound, it was excluded from the list of deregulated metabolites identified by metabolomic analysis.

LC-MS co-migration studies

Metabolomes from GFP-OE and NNMT-OE 769P cells were prepared and analyzed as described above. The identity of endogenous m/z = 137.07 was confirmed by overlapping its extracted ion chromatogram with MS1 ion chromatogram of d4-1MNAinternal standard (m/z = 141.10). Similarly, the identity of endogenous m/z = 385.13 was confirmed by overlapping its extracted ion chromatogram with MS1 ion chromatogram of d4-SAHinternal standard (m/z = 389.16).

MS/MS fragmentation studies

LC-MS/MS analysis was performed on an Agilent 6520 as just described in positive ionization mode. MS and MS/MS data were collected in scanning mode from m/z = 50–2000 and m/z = 50–2500, and a rate of 1.03 spectra/s. The capillary voltage was set to 4 kV, and the fragmentor voltage was set to 100 V. The drying gas temperature was 350 °C, the drying gas flow rate was 11 l/min, and the nebulizer pressure was 45 psi. The collision energy for 1MNA and SAH was 20 V and 5 V, respectively.

Metabolic labeling studies

769P cells were seeded at 1.5 × 106 cells/150 mm dish and were cultured overnight in complete RPMI medium. The next day, the medium was replaced with serum-free medium containing either d4-1MNA (100 μM) or d4-NA (100 μM). Control samples were prepared by incubating cells with the same concentration of authentic compound (either 1MNA or NA). After an additional 4 or 24 h, cellular metabolomes were prepared and analyzed in the untargeted scanning mode as described above. The resultant chromatograms were analyzed by extracting relative m/z values and quantified by calculating the area under the peak. The following deuterated metabolites were detected: d4-1MNA (m/z = 141.10, RT = 5.6 min, pos. mode), d4-NA (m/z = 127.08, RT = 6.1 min, pos. mode), d3-NA (m/z = 126.07, RT = 6.1 min, pos. mode), d3-NMN(m/z = 338.08, RT = 28.2 min, pos. mode), d3-NAD+ (m/z = 665.12, RT = 28.1 min, neg. mode), and d3-NADH(m/z = 667.14, RT = 33.9 min, neg. mode). These metabolites were absent in control cells and their identity was confirmed by co-elution with corresponding authentic metabolites in control cells and authentic standards (1MNA, m/z = 137.07, RT = 5.6 min, pos. mode; NA, m/z = 123.06, RT = 6.1 min, pos. mode; NMN, m/z = 335.06, RT = 28.2 min, pos. mode; NAD+, m/z = 662.10, RT = 28.1 min, neg. mode). The formation of d3-labeled metabolites in NAD+ biosynthetic pathway could be explained by the oxidation/reduction of NAD(H) which would result in the loss of deuterium. Flux of metabolites through the NAD+ pathway would then generate the steady state levels of d3-labeled metabolites that are measured in this assay. Consistent with this model, we observed the time-dependent increase in the ratio of d3-NA/d4-NA in our metabolic labeling studies.

Migration and invasion studies

Migration and invasion assays were performed as described previously8, 15. For 1MNA treatment studies, 0.5 mM 1MNA was added to GFP-OE 769P cells during serum starvation and to the upper and bottom chamber during migration assay.

DNA methylation assay

Genomic DNA was isolated from 769P cells using DNeasy blood & tissue kit from QIAGEN. 1 μg of DNA was degraded into nucleosides using DNA Degradase Plus (Zymo Research). LC separation was achieved with a Sinergy Fusion-RP column (4 μm, 80A, 50 × 4.6 mm, Phenomenex) together with a pre-column (Fusion-RP, 4 × 3.0 mm). Mobile phase A was composed of 100% H2O, and mobile phase B was composed of 100% MeOH. Both solvents were supplemented with 0.1% formic acid to assist ion formation in a positive mode. The flow rate started at 0.1 ml/min for 5 min. The gradient started with 0% B for 5 min and increased linearly to 100% B over 20 min with a flow rate of 0.4 ml/min, followed by an isocratic gradient of 100% B for 2 min at 0.5 ml/min. Then column was equilibrated with 0% B for 3 min at 0.4 ml/min. MS analysis, scanning from m/z = 50–1200, was performed on Agilent 6520 Accurate Mass Q-TOF. 5-Methyl-2′-deoxycytidine (5mdC) content was calculated as [5mdC]/[dG] using external calibration curve as described55.

DNA microarray

mRNAs were isolated (RNeasy Mini Kit, QIAGEN) from NNMT-OE and Y20A-OE 769P cells, reversed transcribed, and hybridized to Affymetrix Human 1.0 ST microarray. Data were then filtered for genes that were upregulated or downregulated (>1.4-fold) in NNMT-OE versus Y20A-OE cells for further analysis.

Real-time (RT)-PCR analysis

mRNAs were isolated using RNeasy Mini Kit (QIAGEN). The cDNAs were synthesized by reverse transcription using the SABiosciences RT2 kit. Obtained cDNAs were added to RT2 SYBR Green mastermix followed by RT-PCR using custom RT2 Profiler PCR Arrays (SABiosciences). RT-PCR was performed on ABI 7900HT cycler (384-well block, Applied Biosystems). Results were normalized to the average of three housekeeping genes, including ACTB, GAPDH and HPRT1. Gene list is shown in table below.